A fusion model combining EEG spectral power and ERPs achieved 80% to 90% classification accuracy for mental workload, performing better than individual models on short data segments.
Combining EEG spectral power and ERPs in a fusion model improves workload estimation accuracy when only short data segments are available.
Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.
Brouwer et al. (2012) studied Mental workload (n=35). Fusion classification model (ERP and frequency power) vs. ERP or frequency power models alone was evaluated on Classification accuracy distinguishing between highest and lowest workload condition. A fusion model combining EEG spectral power and ERPs achieved 80% to 90% classification accuracy for mental workload, performing better than individual models on short data segments.